Hannah Christensen (she/her)

Abstract:

Climate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fidelity, but also by providing new capabilities through emulators for extreme event projections with large ensembles, enhanced detection and attribution methods for extreme events, and advanced climate model analysis and benchmarking. Utilizing this potential requires key machine learning challenges to be addressed, in particular generalization, uncertainty quantification, explainable artificial intelligence and causality. This interdisciplinary effort requires bringing together machine learning and climate scientists, while also leveraging the private sector, to accelerate progress towards actionable climate science.

Abstract:

A machine learning method is used to identify sources of long-term ENSO predictability in the ocean (sea surface temperature (SST) and heat content) and the atmosphere (near-surface zonal wind (U10)). Tropical SST represents the primary source of predictability skill. While U10 does not increase the skill when associated with SST, our analysis suggests U10 alone has a predictive skill comparable to that of SST between 11 and 21 months in advance, from late fall up to late spring. The long-lead signal originates from coupled wind-SST interactions across the Indian Ocean (IO) and propagates across the Pacific via an atmospheric bridge mechanism. A linear correlation analysis supports this mechanism, suggesting a precursor link between anomalies in SST in the western and wind in the eastern IO. Our results have important implications for ENSO predictions beyond 1 year ahead and identify the key role of U10 over the IO.